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PDE-based Facial Animation: Making the Complex Simple
YesDirect parameterisation is among the most widely used facial animation techniques but requires complicated ways to animate face models which have complex topology. This paper develops a simple solution by introducing a PDE-based facial animation scheme. Using a PDE face model means we only need to animate a group of boundary curves without using any other conventional surface interpolation algorithms. We describe the basis of the method and show results from a practical implementation.EPSR
A dynamic texture based approach to recognition of facial actions and their temporal models
In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set
Automatic Content Generation for Video Self Modeling
Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him or herself. Its effectiveness in rehabilitation and education has been repeatedly demonstrated but technical challenges remain in creating video contents that depict previously unseen behaviors. In this paper, we propose a novel system that re-renders new talking-head sequences suitable to be used for VSM treatment of patients with voice disorder. After the raw footage is captured, a new speech track is either synthesized using text-to-speech or selected based on voice similarity from a database of clean speeches. Voice conversion is then applied to match the new speech to the original voice. Time markers extracted from the original and new speech track are used to re-sample the video track for lip synchronization. We use an adaptive re-sampling strategy to minimize motion jitter, and apply bilinear and optical-flow based interpolation to ensure the image quality. Both objective measurements and subjective evaluations demonstrate the effectiveness of the proposed techniques
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second
authorshi
The computer synthesis of expressive three-dimensional facial character animation.
This present research is concerned with the design, development and implementation of three-dimensional
computer-generated facial images capable of expression
gesture and speech.
A review of previous work in chapter one shows that to date
the model of computer-generated faces has been one in which
construction and animation were not separated and which
therefore possessed only a limited expressive range. It is
argued in chapter two that the physical description of the
face cannot be seen as originating from a single generic
mould. Chapter three therefore describes data acquisition
techniques employed in the computer generation of free-form
surfaces which are applicable to three-dimensional faces.
Expressions are the result of the distortion of the surface
of the skin by the complex interactions of bone, muscle and
skin. Chapter four demonstrates with static images and short
animation sequences in video that a muscle model process
algorithm can simulate the primary characteristics of the
facial muscles.
Three-dimensional speech synchronization was the most
complex problem to achieve effectively. Chapter five
describes two successful approaches: the direct mapping of
mouth shapes in two dimensions to the model in three
dimensions, and geometric distortions of the mouth created
by the contraction of specified muscle combinations.
Chapter six describes the implementation of software for
this research and argues the case for a parametric approach.
Chapter seven is concerned with the control of facial
articulations and discusses a more biological approach to
these. Finally chapter eight draws conclusions from the
present research and suggests further extensions
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